class_docstr: CIFAR10 data set from https://www.cs.toronto.edu/~kriz/cifar.html
  
  Arguments:
      path (str): Local path to copy data files.
      normalize (bool): Flag to normalize data.
      whiten (bool): Flag to apply whitening transform.
      pad_classes (bool): Flag to pad out class count to 16
                          for compatibility with conv layers on GPU.
code: def __init__(
      self,
      path=".",
      subset_pct=100,
      normalize=True,
      contrast_normalize=False,
      whiten=False,
      pad_classes=False,
  ):
      super(CIFAR10, self).__init__(
          "cifar-10-python.tar.gz",
          "http://www.cs.toronto.edu/~kriz",
          170498071,
          path=path,
          subset_pct=subset_pct,
      )
      # CIFAR10 load method specific options
      self.normalize = normalize
      self.contrast_normalize = contrast_normalize
      self.whiten = whiten
      self.pad_classes = pad_classes
  
imports: from __future__ import division
  from future import standard_library
  from builtins import str
  import gzip
  import logging
  import numpy
  import os
  import tarfile
  from neon.util.compat import pickle
  from neon.util.compat import pickle_load
  from neon.data.datasets import Dataset
  from neon.data.dataiterator import ArrayIterator
label_desc: Use image node_id 1 to create a new CIFAR10 object from inherited base classes: Dataset with example: obj = CIFAR10(path, subset_pct, normalize, contrast_normalize, whiten, pad_classes)